我已经在.Net Core中编写了一个Cloud Run API,该API从GCS位置读取文件,然后进行非规范化(即,为每行添加更多信息以包括文本描述),然后将其写入BigQuery表。我有两个选择:
如果性能(速度)和成本(货币)是我的目标,那么在这种情况下写BigQuery的最佳方法是什么。在反规范化之前,这些文件每个大约为10KB。每行大约1000个字符。非正规化后,它大约是原来的三倍。在BigQuery中成功加载非规范化文件后,我不需要保留它们。我担心性能以及围绕插入/写入的任何特定BigQuery每日配额。除非您正在执行DML语句,否则我认为不会有任何问题,但是如果我做错了,请更正我。
我将使用将文件上传到存储桶时触发的云功能。
如此普遍,以至于Google 针对JSON文件为此提供了一个回购教程,该教程使用Cloud Functions将数据从Cloud Storage流式传输到BigQuery。
然后,我将从以下示例main.py
文件中进行修改:
def streaming(data, context):
'''This function is executed whenever a file is added to Cloud Storage'''
bucket_name = data['bucket']
file_name = data['name']
db_ref = DB.document(u'streaming_files/%s' % file_name)
if _was_already_ingested(db_ref):
_handle_duplication(db_ref)
else:
try:
_insert_into_bigquery(bucket_name, file_name)
_handle_success(db_ref)
except Exception:
_handle_error(db_ref)
为此,可以接受CSV文件:
import json
import csv
import logging
import os
import traceback
from datetime import datetime
from google.api_core import retry
from google.cloud import bigquery
from google.cloud import storage
import pytz
PROJECT_ID = os.getenv('GCP_PROJECT')
BQ_DATASET = 'fromCloudFunction'
BQ_TABLE = 'mytable'
CS = storage.Client()
BQ = bigquery.Client()
def streaming(data, context):
'''This function is executed whenever a file is added to Cloud Storage'''
bucket_name = data['bucket']
file_name = data['name']
newRows = postProcessing(bucket_name, file_name)
# It is recommended that you save
# what you process for debugging reasons.
destination_bucket = 'post-processed' # gs://post-processed/
destination_name = file_name
# saveRowsToBucket(newRows,destination_bucket,destination_name)
rowsInsertIntoBigquery(newRows)
class BigQueryError(Exception):
'''Exception raised whenever a BigQuery error happened'''
def __init__(self, errors):
super().__init__(self._format(errors))
self.errors = errors
def _format(self, errors):
err = []
for error in errors:
err.extend(error['errors'])
return json.dumps(err)
def postProcessing(bucket_name, file_name):
blob = CS.get_bucket(bucket_name).blob(file_name)
my_str = blob.download_as_string().decode('utf-8')
csv_reader = csv.DictReader(my_str.split('\n'))
newRows = []
for row in csv_reader:
modified_row = row # Add your logic
newRows.append(modified_row)
return newRows
def rowsInsertIntoBigquery(rows):
table = BQ.dataset(BQ_DATASET).table(BQ_TABLE)
errors = BQ.insert_rows_json(table,rows)
if errors != []:
raise BigQueryError(errors)
如果需要的话,仍然需要定义map(row-> newRow)和函数saveRowsToBucket
。